Rebuilding derived content

ABSTRACT

A method by a computing device of a dispersed storage network (DSN) begins by determining whether alternate form data (AFD) exists for a data object. When the alternate form data does not exist, the method continues by identifying a content derivation function in accordance with an AFD policy of the DSN. The method continues by identifying a portion of the data object based on the content derivation function and identifying one or more sets of encoded data slices of a plurality of sets of encoded data slices corresponding to the portion of the data object. The method continues by generating at least a portion of the AFD based on the one or more sets of encoded data slices. The method continues by storing the at least a portion of the AFD within memory of the DSN in accordance with a storage approach.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an example of a DSN storing adata object and one or more alternate form data in accordance with thepresent invention;

FIG. 10 is a schematic block diagram of an example of generatingalternate form data in accordance with the present invention;

FIG. 11 is a schematic block diagram of another example of generatingalternate form data in accordance with the present invention;

FIG. 12 is a schematic block diagram of another example of generatingalternate form data in accordance with the present invention;

FIG. 13 is a schematic block diagram of another example of generatingalternate form data in accordance with the present invention;

FIG. 14 is a schematic block diagram of another example of generatingalternate form data in accordance with the present invention;

FIG. 15 is a schematic block diagram of another example of generatingalternate form data in accordance with the present invention; and

FIG. 16 a logic flow diagram illustrating an example of a method ofdetermining whether alternate form data exists in accordance with thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data 40 on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSTN memory 22for a user device, a group of devices, or for public access andestablishes per vault dispersed storage (DS) error encoding parametersfor a vault. The managing unit 18 facilitates storage of DS errorencoding parameters for each vault by updating registry information ofthe DSN 10, where the registry information may be stored in the DSNmemory 22, a computing device 12-16, the managing unit 18, and/or theintegrity processing unit 20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSTN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of an example of a DSN storing adata object and one or more alternate form data of the data object(e.g., AFD1 and 2). As discussed with reference to FIGS. 3-5, the dataobject 90 is dispersed storage error encoded into a plurality of sets ofencoded data slices (EDSs). The alternate form data (AFD) 96 and 98 arerepresentations of original data and are generated by content derivationfunctions 92 and 94. A content derivation function is generally afunction to create a representation of the data object or a portionthereof (e.g., a thumbnail, facial recognition, sample of a video,sample of audio, text summary, caption, etc.). For example, AFD may bean image of a series of images or a portion of an image (e.g., a face,an object, a corner, etc.).

As a specific example, AFD1 96 is low resolution image (e.g., thumbnail)that is created by using a content derivation function on a highresolution data object image. The AFD1 96 is then dispersed storageerror encoded in accordance with dispersed storage error encodingparameters (e.g., using an encoding function (e.g., CauchyReed-Solomon)) to produce a set of alternate form data (AFD) slices. Theset of AFD slices (e.g., EDS 1_1_A1-EDS 4_1_A1) is provided AFD slicenames and stored in storage units #1-5 36.

As another example, AFD2 98 is a portion of the image (e.g., a corner, aface in a crowd, etc.) that is created by using a content derivationfunction on the image. For example, for data object 90 (e.g., a group ofpictures), a content derivation function is run to identify alternateform data 98 (e.g., a face that is user defined as “Mary”). The AFD2 98is then dispersed storage error encoded in accordance with dispersedstorage error encoding parameters to produce a set of alternate formdata (AFD) slices (e.g., EDS 1_1_A2 through EDS 5_1_A2). The set of AFDslices (e.g., EDS 1_1_A2-EDS 5_1_A2) are provided AFD slice names andstored in DSN memory (e.g., storage units #1-5 36).

An issue arises when AFD should exist (e.g., per a policy), but does notor is corrupted. For example, a computing device determines that AFD1 ismissing when it sends a listing request to an alternate form data indexand receives a listing response from the alternate form data index thatdoes not include AFD1 and the policy indicates AFD1 should exist. Asanother example, a computing device determines AFD2 is corrupted when itsends a read request to storage unit 1 regarding an alternate form dataslice (e.g., EDS 1_1_A2) and receives a read error response. As anotherexample, the computing device determines AFD2 is corrupted when a localchecksum for EDS 1_1_A2 does not substantially match a previously storedchecksum for EDS 1_1_A2 stored in SU #1.

FIGS. 10 and 11 are a schematic block diagram of an example of utilizingencoded data slices of the encoded data object to generate encoded dataslices of alternate form data (AFD). In an example of operation, acomputing device (e.g., of a dispersed storage network (DSN)) determineswhether alternate form data (AFD) exists for a data object. Note thatthe AFD is a content derivation of the data object. When the computingdevice determines that the alternate form data does not exist, itidentifies a content derivation function (e.g., by checking with an AFDpolicy of the DSN). The computing device then uses the contentderivation function to identify a portion of the data object (e.g., EDS1_2_D through EDS 5_2_D) to generate the AFD (e.g., EDS 1_1_A1 throughEDS 4_1_A1).

As a specific example, the computing device retrieves and decodes atleast a decoded threshold number of encoded data slices EDS 1_2_Dthrough EDS 5_2_D to recover the corresponding data segment of the dataobject. The computing device then dispersed storage error encodes therecovered data segment to produce AFD slices EDS 1_1_A1-EDS 4_1_A1. Thecomputing device then creates AFD slice names for AFD slices EDS 1_1_A1through EDS 4_1_A1 and sends at least a decoded threshold of AFD slicesto DSN memory for storage (e.g., SU #1-5 36).

FIG. 12 is a schematic block diagram of a specific example of utilizingencoded data slices of the encoded data object to generate encoded dataslices of alternate form data (AFD). As an example, a computing devicecopies encoded data slices EDS 1_2_D-EDS 4_2_D to create AFD slices EDS1_1_A1-EDS 4_1_A1. The computing device also generates AFD slice names(SN 1_1_A1-SN 4_2_A1) for the AFD slices (EDS 1_1_A1-EDS 4_1_A1). Havinggenerated the AFD slice names, the computing device sends the copiedslices and AFD slices names to a set of storage units of the dispersedstorage network (DSN). As another example, the computing devicedetermines to store a decode threshold number (e.g., 3) of the copiedEDSs and, in accordance with an AFD policy, stores EDS 1_1_A1, EDS2_1_A1 and EDS 4_1_A1 in DSN memory.

FIG. 13 is a schematic block diagram of another specific exampleutilizing encoded data slices of the encoded data object to generateencoded data slices of alternate form data (AFD). In this example, thecomputing device is not copying the set of EDSs, just pointing to themto create the AFD. As a specific example, the computing device generatesslice names SN 1_1_A1-SN 4_1_A1 of the AFD to be identical to slicenames SN 1_2_D-SN 4_2_D. The computing device then creates a directoryentry for the AFD to include the AFD slice names. Note that as created,the AFD slice names point to at least some of the set of encoded dataslices. As an example, the AFD slices name point to four of the fiveencoded data slices (e.g., EDS 1_2_D-EDS 5_2_D) of FIG. 11.

FIG. 14 is a schematic block diagram of another specific example ofutilizing encoded data slices of the encoded data object to generateencoded data slices of an (AFD). As illustrated, the data object is animage with sixteen pixel blocks. Each pixel block may include one ormore sets of encoded data slices. As an example, a computing devicegenerates the alternate form data in the form of a lower resolutionimage. For example, the content derivation function determines whichpixel blocks or which sets of encoded data slices to select in order tocreate an AFD image with four pixel blocks. As a specific example, apixel block is dispersed storage error encoded into 10 segments with apillar width number of 16. The computing device, in accordance with thecontent derivation function, creates an AFD pixel block of 10 segmentswith a pillar width number of 12. In one instance, the computing devicegenerates 12 AFD slice names that are substantially similar to slicenames for 12 encoded data slices of each of the 10 segments of theoriginal pixel block. The computing device then creates a directoryentry for the AFD to include the 120 AFD slice names.

FIG. 15 is a schematic block diagram of another specific example ofutilizing encoded data slices of the encoded data object to generateencoded data slices of alternate form data (AFD). As illustrated, a dataobject may be a series of images (e.g., a movie). A computing deviceselects (e.g., in accordance with a content derivation function) animage of the series of images and generates the AFD as a lowerresolution image.

FIG. 16 is a logic diagram of a method of determining whether alternateform data (AFD) exists. The method begins with step 100, where acomputing device determines whether alternate form data (AFD) exists fora data object. As an example, the computing device accesses an AFDpolicy to determine that the AFD is to exist. As another example, a DSprocessing unit scans dispersed storage network (DSN) memory forinstances where AFD should exist for a data object by reading alloriginal data metadata objects and checking for an AFD indicator.

When the AFD does not exist, the method continues to step 102, where thecomputing device identifies a content derivation function in accordancewith an AFD policy of the DSN. As an example, the computing deviceselects the content derivation function from a plurality of contentderivation functions in accordance with the AFD policy regarding thedata object.

The method continues to step 104, where the computing device identifiesa portion of the data object based on the content derivation function.As a specific example, the computing device identifies a human face in adata object image. As another specific example, the computing deviceidentifies an object (e.g., a table) in a data object image. The methodcontinues to step 106, where the computing device identifies one or moresets of encoded data slices of a plurality of sets of encoded dataslices that correspond to the portion of the data object. Note the dataobject is dispersed storage error encoded to produce the plurality ofsets of encoded data slices.

The method continues to step 108, where the computing device generatesat least a portion of the AFD based on the one or more sets of encodeddata slices. As a specific example, the data object is an original imageand the computing device uses the content derivation function togenerate a lower resolution image (e.g., thumbnail) from the originalimage to create the AFD. As another specific example, when the dataobject is a series of images, the computing device selects (e.g., inaccordance with the content derivation function) an image of the seriesof images and generates a lower resolution image from the selected imageto create the AFD.

The method continues to step 110, where the computing device stores, inaccordance with a storage approach, the at least a portion of the AFDwithin memory of the DSN. For example, when the storage approachincludes a directory manipulation, the computing devices generates AFDslice names, for the at least the portion of the AFD, that aresubstantially similar to slice names of the one or more sets of encodeddata slices. The computing device creates a directory entry for the AFDto include the AFD slice names. As another example, when the storageapproach includes a copy and paste storage operation, the computingdevice copies the one or more sets of encoded data slices, generates AFDslice names for the copied one or more sets of encoded data slices, andstores, in accordance with the AFD slice names, at least a decodethreshold of encoded data slices for each of the copied one or more setsof encoded data slices in a set of storage units of the DSN. As afurther example, when the storage approach includes a new file storageoperation, the computing device retrieves the one or more sets ofencoded data slices, decodes the one or more sets of encoded data slicesto recreate the portion of the data object, and dispersed storage errorencodes the recreated portion of the data object to produce one or moresets of AFD slices that correspond to the at least a portion of the AFD.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method comprises: determining, by a computingdevice of a dispersed storage network (DSN) whether alternate form data(AFD) exists for a data object, wherein the AFD is a content derivationof the data object; and when the alternate form data does not exist:identifying a content derivation function in accordance with an AFDpolicy of the DSN; identifying a portion of the data object based on thecontent derivation function; identifying one or more sets of encodeddata slices of a plurality of sets of encoded data slices correspondingto the portion of the data object, wherein the data object is dispersedstorage error encoded to produce the plurality of sets of encoded dataslices; generating at least a portion of the AFD based on the one ormore sets of encoded data slices; and storing, in accordance with astorage approach, the at least a portion of the AFD within memory of theDSN.
 2. The method of claim 1, wherein the determining whether the AFDexists comprises: accessing the AFD policy to determine that the AFD isto exist.
 3. The method claim 1, wherein the identifying the contentderivation function comprises: selecting the content derivation functionfrom a plurality of content derivation functions in accordance with theAFD policy regarding the data object.
 4. The method of claim 1, whereinthe content derivation function comprises: generating a lower resolutionimage from an original image to create the AFD, wherein the data objectis the original image.
 5. The method of claim 1, wherein the contentderivation function comprises: when the data object is a series ofimages: selecting, in accordance with the content derivation function,an image of the series of images; and generating a lower resolutionimage from the selected image to create the AFD.
 6. The method of claim1, where the generating the at least a portion of the AFD comprises:generating AFD slice names for the at least the portion of the AFD,wherein the AFD slice names are substantially similar to slice names ofthe one or more sets of encoded data slices; and creating a directoryentry for the AFD to include the AFD slice names, wherein the storageapproach includes a directory manipulation.
 7. The method of claim 1,where the generating the at least a portion of the AFD comprises:copying the one or more sets of encoded data slices; generating AFDslice names for the copied one or more sets of encoded data slices; andstoring, in accordance with the AFD slice names, at least a decodethreshold of encoded data slices for each of the copied one or more setsof encoded data slices in a set of storage units of the DSN, wherein thestorage approach includes a copy and paste storage operation.
 8. Themethod of claim 1, where the generating the at least a portion of theAFD comprises: retrieving, the one or more sets of encoded data slices;decoding the one or more sets of encoded data slices to recreate theportion of the data object; and dispersed storage error encoding therecreated portion of the data object to produce one or more sets of AFDslices, wherein the one or more sets of AFD slices corresponds to the atleast a portion of the AFD, wherein the storage approach includes a newfile storage operation.
 9. A computing device of a dispersed storagenetwork (DSN) comprises: an interface; memory; and a processing moduleoperably coupled to the interface and memory, wherein the processingmodule is operable to: determine whether alternate form data (AFD)exists for a data object, wherein the AFD is a content derivation of thedata object; and when the alternate form data does not exist: identify acontent derivation function in accordance with an AFD policy of the DSN;identify a portion of the data object based on the content derivationfunction; identify one or more sets of encoded data slices of aplurality of sets of encoded data slices corresponding to the portion ofthe data object, wherein the data object is dispersed storage errorencoded to produce the plurality of sets of encoded data slices;generate at least a portion of the AFD based on the one or more sets ofencoded data slices; and store, in accordance with a storage approach,the at least a portion of the AFD within memory of the DSN.
 10. Thecomputing device of claim 9, wherein the processing module determineswhether the AFD exists by: accessing the AFD policy to determine thatthe AFD is to exist.
 11. The computing device of claim 9, wherein theprocessing module identifies the content derivation function by:selecting the content derivation function from a plurality of contentderivation functions in accordance with the AFD policy regarding thedata object.
 12. The computing device of claim 9, wherein the processingmodule is further operable to perform the content derivation functionby: generating a lower resolution image from an original image to createthe AFD, wherein the data object is the original image.
 13. Thecomputing device of claim 9, wherein the processing module is furtheroperable to perform the content derivation function by: when the dataobject is a series of images: selecting, in accordance with the contentderivation function, an image of the series of images; and generating alower resolution image from the selected image to create the AFD. 14.The computing device of claim 9, where the processing module generatesthe at least a portion of the AFD by: generating AFD slice names for theat least the portion of the AFD, wherein the AFD slice names aresubstantially similar to slice names of the one or more sets of encodeddata slices; and creating a directory entry for the AFD to include theAFD slice names, wherein the storage approach includes a directorymanipulation.
 15. The computing device of claim 9, where the processingmodule generates the at least a portion of the AFD by: copying the oneor more sets of encoded data slices; generating AFD slice names for thecopied one or more sets of encoded data slices; and storing, inaccordance with the AFD slice names, at least a decode threshold ofencoded data slices for each of the copied one or more sets of encodeddata slices in a set of storage units of the DSN, wherein the storageapproach includes a copy and paste storage operation.
 16. The computingdevice of claim 9, where the processing module generates the at least aportion of the AFD by: retrieving the one or more sets of encoded dataslices; decoding the one or more sets of encoded data slices to recreatethe portion of the data object; and dispersed storage error encoding therecreated portion of the data object to produce one or more sets of AFDslices, wherein the one or more sets of AFD slices corresponds to the atleast a portion of the AFD, wherein the storage approach includes a newfile storage operation.